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Sensory Processing in the Brain and Evoked Potentials
Sensory Processing in the Brain and Evoked Potentials D. M. MacKAY Department of Communication and Neuroscience, University of Keele. Keele, Stafls. ST5 5BG (Great Britain)
In thinking over the topic of this lecture, the image that came to mind was that of tunnelling the Alps. I remember as a child being impressed by the precision with which those making a tunnel from the north side were able by dead reckoning to proceed so accurately to meet those tunnelling from the south side that only a fraction of a metre separated the two when they met. Over the past 3 decades, we have all been making strenuous efforts to form a working link between those studying sensory processing in the brain at the single unit level and those studying sensory evoked potentials. The enterprise is still at a relatively early stage, and pessimism is sometimes expressed as to whether our respective tunnels will ever meet. Are we even starting at the right levels, let alone heading in convergent directions? This is the question that challenges us in the present session. I regard it as very much an unresolved question; but I would like to offer one or two suggestions as to ways in which the process of fruitful convergence might be accelerated. Because Dr. McCallum will be dealing mainly with the auditory system in his review, I shall take most of my examples from our recent work at Keele on the visual system, with just a brief reference to auditory information processing at the end. THE IMPORTANCE O F GLOBAL PROPERTIES OF THE VISUAL FIELD Visual neurophysiology took a great leap forward when Hubel and Wiesel (1959, 1962), working on cat, and Lettvin et al. (1959), working on the frog, discovered that many single units in the visual system are specifically sensitive to bars or edges of luminance-contrast with specific orientations on the retina. In area 17 of cat, for example, Hubel and Wiesel identified cells which they called respectively “simple”, “complex” and “hypercomplex”. Simple cells had relatively restricted receptive fields, in which elongated excitatory and inhibitory zones flanked one another and gave rise to a strong preference for appropriately oriented bars or edges of luminance moving across them. Complex cells, which also had well-defined orientation preferences, were responsive over a wider area and were presumed to depend on simple cells for their inputs. In consequence of these discoveries and others like them, most of us in recent years have worked with a model of the primary visual cortex as a serial information processing mechanism. Simple and complex cells were thought of as serially related “feature detectors”, each of which signified by its firing one particular geometrical detail of the
246 figure of excitation on the retina. Models of pattern recognition were canvassed in which these simple and complex cells served as the first stages in a hierarchic pattern classifier leading to unique specificity of single cells for the most complex patterns. On the other hand, it has long been pointed out by such writers as Gibson (1950) that for many biologically significant purposes the visual system ought to be sensitive to more global properties of the visual field, such as texture density and gradients of texture density. He noted, for example, that an object camouflaged against a background of similar texture can be instantly perceived, and its form recognized, if it moves against its background so as to generate discontinuities of texture-motion on the retina. This, together with the discovery of a number of psychaphysical phenomena in which texture density played a role somewhat analogous to luminance (MacKay, 1973, 1974), led me to wonder how the simple and complex cells of area 17 might respond to visual patterns presented as contrasts of texture motion. If they were the classifying elements in pattern recognition, would they show the same orientation specificity for patterns of texture contrast as for patterns of luminance contrast? My colleague Dr. Peter Hammond and I are still at an early stage in this investigation, and what follows are only samples from an on-going story (Hammond and MacKay, 1975, 1977, 1978; Groos et al., 1976; Hammond, 1978). RESPONSES OF CAT STRIATE CELLS TO TEXTURED STIMULI Fig. 1 shows some examples of the stimuli we use. These are produced electronically on a CRT display capable of a basic picture frame rate up to 400/sec. Our first tests employed bars of “static visual noise”, variable in orientation and dimensions, which could be swept to and fro over a statistically similar static noise background. Cats were lightly anaesthetized with N,O/O, mixtures supplemented with pentobarbitone. What we found may be summarized as follows: (1) Simple cells were unresponsive to all forms of visual noise presented alone, except when individual blobs of texture were large enough to function as luminance stimuli in their own right. About 70%, however, showed a change in responsiveness to conventional black-and-white bar stimuli, when these were presented against backgrounds of static noise which moved with the bar rather than reniaining stationary. Luminancebar responses were depressed by background texture motion in the majority of cells (84%), but were actually enhanced in some instances (16%). (2) In contrast, all complex cells were found to respond to some extent to bars of static visual noise moving over stationary backgrounds of similar texture. Significantly, however, most of them showed no sensitivity to the orientation of the stimulus boundary. They responded as well or better to motion of a whole field of static noise. This showed that (a) whatever part simple and complex cells in area 17 may play in the recognition of patterns outlined by luminance contrast, they do not play the same role in relation to recognition of patterns outlined by texture-motion contrast; (b) since complex cells can be excited (to a greater or lesser extent) by stimuli to which simple cells are unresponsive, most complex cells must receive a visual input independent of that from simple cells. (3) In a number of cases, the ocular dominance of complex cells was different for bar stimuli and for visual texture. (4) Complex cells fell into two overlapping groups with regard to their relative
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Fig. I . Examples of textured stimuli used.
sensitivity to light or dark bars and visual noise. Extreme examples were insensitive to conventional light or dark bar or edge stimuli, while responding briskly to moving noise. The optimal velocity for noise was generally lower than for bar stimuli. ( 5 ) In many complex cells, the preferred directions for motion of noise and of an optimally oriented black/white bar were strikinglydissimilar. Fig. 2 shows some examples of the responsiveness as a function of direction of stimulus motion for each class of stimuli. In a number of cases the polar diagram for visual noise showed two maxima, which might be separated by 90"or more. Fig. 3 gives some indication of the range of directional disparities observed. (6) The greatest sensitivity to textured stimuli was found among deep-layer complex cells. We have also found these cells to be most tolerant of misalignment of the components of a stimulating luminance line. Superficial-layer complex cells were found to be less tolerant of misalignment of stimulus elements and were less responsive to moving noise.
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Fig. 3. Histogram showing the range of disparitiesin preferred direction of motion found by Hammond (1978).
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It follows from ill this that the firing of many such complex cells cannot in isolation signify to the central nervous system either the geometrical orientation or the direction of motion of the exciting stimulus. This does not necessarily mean that they have no function in relation to pattern recognition, but it does mean that they can function in this respect only in co-operation with their neighbours, possibly on a basis of crosscorrelation of firing patterns with respect to space and time. MODULATION O F RESPONSIVENESS OF SIMPLE CELLS BY BACKGROUND MOTION In order to investigate the effect of moving backgrounds on the responsiveness of simple cells, Hammond and MacKay (1978) have been using electronically generated stimuli of the sort schematized in Fig. 4. A black or white bar is oscillated continuously (ljsec) at the preferred orientation and location of the cell to be investigated. A moveable patch (shown occupied by a chequerboard in Fig. 4) is alternately held stationary and moved with the stimulus. The general texture background can also be moved with the stimulus or held stationary. Fig. 5 shows the result of one experiment in which the patch was centred on the receptive field, and its size varied in the direction of the length of the stimulating bar (i.e., along the receptive field axis). It will be seen that the suppressive effect of the moving background continues to increase for a patch much longer than the receptive field as conventionally defined. Fig. 6 shows the result of keeping the patch constant in size and exploring its effectiveness by displacing it along the length axis of the field. To our surprise, it turned out in a number of cases (though not in all) that a moving patch well outside the conventional receptive field could actually enhance the responsiveness of a simple cell, although suppressing it when closer to the centre of the receptive field.
Fig. 4. Sample of stimuli used to investigate modulation of responsiveness of simple cells by areas of moving textured background.
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Fig. 6 . Modulating power of a narrow patch as a function of distance from receptive field centre. Receptive field length approx. 1".
IMPLICATIONS FOR EP WORK Preliminary though some of them are, these results have a number of implications relevant in the present context. First, in line with other evidence (Cleland et al., 1976), they indicate that we must reckon with parallel as well as serial processing of information in the primary visual cortex. While they do nothing to refute the accepted belief that most complex cells are hierarchically dependent on simple cells, they do suggest that most complex cells also receive an input independent of simple cells. Secondly, they show that different classes of visual stimulus may excite the binocularly driven cell population to differing extents. In other words, some cells may be dominated by bar stimuli to one eye but equally sensitive to texture stimuli in either eye. Thirdly, cells excited by bars moving in one direction may also be excitable by patterns of random texture moving in quite different directions. Fourthly, even simple cells may receive extensive lateral influences from neighbouring cells, so that their response characteristics can be modulated from a considerable distance. In general, then, we must beware of thinking of even the primary visual cortex as an assembly of independent descriptors of the geometrical features lying within sharply
25 1
localized windows. We must be prepared for elements of the visual network to interact co-operatively and to show specific responses to global properties of the stimulus field. All this throws a new light on the possible usefulness of evoked brain potentials as reflectors of such co-operative interactions. I would like now to turn to a few examples, also drawn from recent work in our laboratory, of EP data which point in the same direction. Most of these concern early (5G200 msec) components of the visual evoked response, though I will touch on one auditory example at the end. SENSITIVITY OF PATTERN-EVOKED RESPONSES TO GLOBAL FEATURES Although the electrogenesis of the visual EP is still a matter of debate, it might have seemed plausible to expect the patternevoked components of the response to reflect something like the total length of contour in the visual field. Fig. 7, however, from the early work of Jeffreys (1969) shows that this expectation is not fulfilled. Instead, Jeffreys found that the removal of contour from a uniform pattern such as (b) in Fig. 7, so as to give a more broken outline (d), enhanced the evoked response. Isolated squares (f) produced a larger respbnse than a chequerboard (g), and so on. For the generator of this EP component, then, what seems to matter is not the number of “contour-detectors’’ excited, but what we might call the “brokenness” - a more global property - of the stimulating pattern. Despite appearances, even the early response complex shown in Fig. 7 is not unitary. It was discovered by Spekreijse (1966) that the “steady-state” potentials evoked by rhythmic presentation of a given pattern could be considerably reduced by pre-exposing an outline of the same pattern drawn with thin lines. An analogous finding has been 250 m s
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Fig. 7. Averaged,scalp responses to lower half-field presentation of patterns shown, of varying degrees of “brokenness”. Active electrodes 5 cm above inion, 6 an apart. (Courtesy of D. A. Jeffreys.)
252 exploited by Jeffreys (1977) in relation to transient VEPs to show that the positive and negative peaks in Fig. 8 are due to the superposition of independently variable components, named by him C1 and C2, which turn out to have different scalp distributions (Jeffreys, 1971). When a pattern of isolated squares is presented tachistoscopically after pre-exposure to the outlined squares shown in Fig. 8c, there is relatively little diminution of the first peak, but the second is heavily attenuated. Conversely, when the outlined figure alone is tachistoscopically presented, the evoked response (b) contains a large C2 component but practically no C1. The same figure shows that although a grating of horizontal bars (d) evokes only a small second peak, the first peak is of comparable magnitude. Pre-exposure to an outline of the grating, as shown at Fig. 8e, again attenuates C2 selectively. By varying the relative location of the pre-adapting outlines, Jeffreys has shown that exact superposition is not necessary. Moreover, as shown in Fig. 9, a uniform grid of lines does not have the same adapting effect on the EP to isolated squares, even though its contours cover all the contours of the stimulating figure. This shows that the preadapting effect is not due simply to pre-excitation of contour detectors. As shown in Fig. 9, even the component C2 for a chequerboard stimulus is not suppressed by a grid of lines (although C3 is much attenuated). Once again, the implication seems to be that more global properties of the figure of excitation, rather than its local specification in terms of the presence or absence of contours, play a part in determining the response. What then of the earlier component Cl? The fact that it shows relatively less adaptation after pre-exposure of stimulus outlines suggests that part of it originates in a sub-population responsive to modulation of luminance at lower spatial frequencies. Jeffreys and Smith (1979) have confirmed this hypothesis by measuring the adapting effect (on C1) of pre-exposure to patterns with luminance components having similar orientations at low spatial frequencies but disparate orientations at high frequencies. Fig. 10 for example shows that C1 for a square-wave grating was reduced more by preexposure to a chequerboard at 45" (when the fundamental frequencies had the same Clll
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Fig. 10. Typical transient VEP waveform elicited: (a) by a horizontal 2.0 c/degree grating in the left half of the field without adaptation; (b) after adaptation to a horizontally orientated chequerboard; (c) after adaptation to an obliquely orientated chequerboard.
orientation) than when the edges of the chequerboard were parallel to those of the grating. Fig. 11 shows that this also applied when the adapting and evoking stimuli were interchanged*. Jeffreys has also found the method of selective adaptation useful in identifying corresponding components of the EP complex from stimuli presented to different retinal areas. Pattern EPs from upper and lower half-fields, for example, show a characteristic reversal of polarity (at the same latency) in many subjects. Jeffreys and Axford (1972) showed by a simple model that for C1 this reversal could be attributed to the infolding of the striate cortex around the calcarine fissure. Lehmann et al. (1977) and Lehmann and *Since this paper was presented, DeValois et al. ( J . Physiol. (Lond.), 1979, 291: 483-505) have published an investigation of simple and complex cells of striate cortex using grating and chequerboard stimuli, with somewhat analogous results. This is encouragingly consonant with the hypothesis of Jeffreys and Axford (1972) that C1 originates in striate cortex, though it may be questioned whether these findings oblige us to follow DeValois et al. in considering single cells as “spatial-frequency filters” rather than “edge-detectors”.
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Skrandies (1979) have recently suggested that it could instead be due to large differences in latency between upper- and lower-field responses; but Jeffreys and Smith (1979), using the pre-adaptation method, have been able to confirm that the components with similar adaptive characteristics do have the same latencies but opposite polarities in the two cases (Fig. 12).
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Fig. 12. Comparison of sensitivity of upper and lower half-field VEPs to preexposure of outlined patterns. Pre-exposure selectively affects peaks of opposite polarity but the same latency, suggesting that only the orientations of the sources stimulated by upper and lower half-fields are different (see Jeffreys and Smith, 1979).
255 One way of interpreting the attenuation of C2 (and C3) by pre-exposure to pattern outlines would be to postulate that these components were generated by mechanisms specific for recognition of the pattern as such, which would presumably be activated by the pre-exposure, and so become adapted. This hypothesis, however, is not supported by an experiment which was suggested by the work with Hammond described above. Instead of using thin black lines to outline the pre-exposed adapting pattern, I used patterns outlined by the contrast between static and dynamic random visual noise, generated on the electronic display used for our neurophysiological experiments (MacKay, 1977). This made it possible to pre-expose the subject to a pattern of isolated squares without any contrast in mean luminance (Fig. 13). The response to the subsequent stimulus pattern of black isolated squares was indeed reduced (Fig. 14a4), but when the pre-adapting noise-contrast pattern itself was used as an evoking stimulus the resulting waveform (e) had not at all the same time course as the EP to black squares*. Furthermore, the EP to a grating of dynamic noise stripes on an isoluminant static noise background (f) was as large as that to the isolated noise squares of Fig. 13 again in contrast to the EP to a black-and-white grating, which was much smaller than that to black squares (compare Fig. 14h and i). This does not mean, however, that only the area of texture matters, and not its pattern; for the EPs to a whole-field transition
Fig. 13. Typical pattern of static noise (SN) squares, each subtending0.6", on a background of dynamic noise (DN), here shown as uniform grey. Fixation at point X, top centre. *The EP to thinly outlined squares is more similar in form but different in latency (Jeffreys, personal communication).
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Fig. 14. a, c: VEPs to 25 msec presentations of pattern of isolated 0.6" black squares as in Fig. 12a, after preexposure to (left to right): (i) dynamic noise (DN); (ii) matching pattern of squares of static noise (SN) on DN, (iii) ditto but DN squares on SN; (iv) SN. Subjects (a) DAJ and (c) IEB. Amplifier time constant 0.3 sec. b, d: as above, but squares subtending0.3" with 0.3" gaps. e: VEPs to 60 msec presentations of (left) isolated 0.6" SN squares on steady DN background; (right) ditto, but DN squares on steady SN. Subject DAJ. f: as e, but with unbroken horizontal strips instead of squares. g: as e, but whole field changing from (left) DN to SN; (right) SN to DN. h: VEPs to 25 msec presentations of isolated 0.6"black squares with uniform white pre-exposure field. Subject DAJ (compare e). i: as h, but with unbroken black horizontal strips instead of squares (compare f). Two successive averages superimposed in each case.
257 from static to dynamic noise or vice versa are significantly smaller (Fig. 14g). Complex though this picture is, it at least shows that the mechanism responsible for the adapting response to pre-exposure is not indifferent to the mode of presentation of the preadapting pattern, and hence is unlikely to be the one responsible for the recognition of geometrical form regardless of mode of presentation.
EP INDICATIONS O F SENSORY CROSS-CORRELATION The experiments of Lehmann and Julesz (1978) have shown that significant EPs can be elicited by changes in the perceptual depth (retinal image disparity) of stereoscopically presented surfaces of dynamic noise. This raises the question whether more basic forms of sensory cross-correlation, which there are theoretical reasons for expecting to be ubiquitous in the CNS (MacKay, 1978b), are accompanied by detectable changes in scalp potential. On the visual front, Lehmann et al. (1978) have reported that when a field of dynamic noise in one eye is switched from incoherence to coherence with a similar field in the other eye, a sustained (anterior negative) shift in scalp potential occurs. Similar results have been found independently by Julesz (Julesz, B., personal communication) and myself, and Fig. 15 shows a typical result from one of my subjects. In preliminary experiments on the auditory system I have also found analogous evoked responses to a transition from incoherence to coherence, or vice versa, between random noise inputs to the two ears (Fig. 16). Again the change to coherence evokes an anterior-negative potential shift. It can be seen from the lower traces in Fig. 16 that the responses to a 100 msec pulse of silence in noise (a), or noise in silence (b), are quite different in form and latency. COH
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Fig. 15. Distribution of averaged scalp potentials (N = 80) evoked by transitions between coherence (COH) and incoherence (INC) of dynamic visual noise fields (30 frames/sec) in left and right eyes.
258 COH
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Fig. 16. Upper traces: average (N = 100) of anterior/posterior midline potentials evoked by a 100 msec pulse of coherence (COH) or incoherence (INC) between noise signals to left and right ears (two successive averages superimposed). Lower traces: controls using a 100 msec pulse of (a) silence out of a background of incoherent binaural noise; (b) incoherent binaural noise against a silent background.
DISCUSSION Much of the neurophysiological work I have described is still at an early stage; but I would like to offer the following points for discussion: (a) There is now clear evidence of parallel as well as serial processing of visual information even as early as the primary projection area 17. (b) Different classes of stimuli may have different effective “weights” for binocular units in striate cortex, e.g., some cells may even be functionally monocular for black-and-white bar stimuli yet binocular for textured stimuli. (c) The same cell may have different preferred directions of motion for different geometrical classes of stimuli (e.g. bars versus static noise). (d) Both simple and complex cells of area 17 may receive lateral inputs and be dynamically dependent on relatively remote neighbours for their detailed response characteristics from moment to moment. This evidence should perhaps warn us against thinking even of the primary visual cortex as an assembly of independent “descriptors looking through windows” (featuredetecting cells whose firing would signifv to the CNS the presence of their favourite feature). Doubtless most of these cells have preferred features, and their firing surely contributes information as to the external stimuli exciting them; but it seems likely that the decoding of that information requires much computational interaction with signals from other cells of the same level. We would also be unwise, I think, to restrict our idea of such interaction to simple notions of “sharpening”, “peak-picking’’ and the like - as if the cells were “really” trying to do a feature-detecting operation but were bungling their selectivejob. There are many hints from perceptual phenomena (MacKay, 1957, 1970, 1978a,b; Julesz, 1971) that we must be prepared to find the neural network responding co-operatively to global properties of the stimulus field. There are reasons for suspecting that this co-operativity may in particular cases amount to transient rearrangement of local couplings (MacKay, 1957, 1970, 1978a, 1979; MacKay et al., 1979).
259 All this, it seems to me, gives a new importance to the EP recording method, and a fresh incentive towards its refinement, not just as a second best to the recording of single action potentials, but as in principle one of the few ways in which we can hope to detect and follow neurophysiologically the progress of spatially extensive co-operative processes in the neural network. Without some such means, it seems more than possible that a realistic understanding of sensory information processing will be denied us. ACKNOWLEDGEMENTS
I thank my colleagues Peter Hammond and Aled Jeffreys both for helpful comments on this manuscript and for the major contributions they have made to its illustrative material. The support of the Medical and Science Research Councils is gratefully acknowledged. REFERENCES Cleland, B. G., Levick, W. R., Morstyn, R. and Wagner, H.G. (1976) Lateral geniculate relay of slowly conducting retinal afferents to cat visual cortex. J. Physiol. (Lond.), 255: 299-320. Gibson, J. J. (1950) The Perception of the Visual World. Houghton MifAin, Boston, Mass. Groos, G. A., Hammond, P. and MacKay, D. M. (1976) Polar responsiveness of complex cells in cat striate cortex to motion of bars and of textured patterns. J. Physiol, (Lond.), 260: 47-48P. Hammond, P. (1978) Directional tuning of complex cells in area 17 of the feline visual cortex. J. Physio/. (Lond.), 285: 479491. Hammond, P. and MacKay, D. M. (1975) Differential responses of cat visual cortical cells to textured stimuli. Exp. Bruin Res., 22: 427430. Hammond, P. and MacKay, D. M. (1977) Differential responsiveness of simple and complex cells in cat striate cortex to visual texture. Exp. Bruin R e x , 3 0 275-296. Hammond, P. and MacKay, D. M. (1978) Modulation of simple cell activity in cat by moving textured backgrounds. J. Physiol. (Lond.), 284: 1 17P. Hubel, D. H. and Wiesel, T. N. (1959) Receptive fields of single neurones in the cat’s striate cortex. J. Physio/. (Lond.), 148: 576591. Hubel, D. H. and Wiesel, T. N. (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. (Lond.), 160: 106154. Jeffreys, D. A, (1969) Discussion. In Evoked Bruin Potentiuls us Indicators of Sensory Information Processing, D. M. MacKay (Ed.), Neurosci. Res. Bull., 7 216. Jeffreys, D. A. (197 1) Cortical source locations of pattern-related visual evoked potentials recorded from the human scalp. Nature (Lond.), 229: 502-504. Jeffreys, D. A. (1977) The physiological significance of pattern visual evoked potentials. In Visual Evoked Potentials in Man: New Developments. J. E. Desmedt (Ed.), Clarendon Press, Oxford, pp. 134-167. Jeffreys, D. A. and Axford, J. G. (1972) Source locations of pattern-specific components of human visual evoked potentials. I. Component of striate cortical origin. Exp. Bruin Res., 16: 1-21. Jeffreys, D. A. and Smith, A. T. (1979) The polarity inversion of scalp potentials evoked by upper and lower half-field stimulus patterns: latency or surface distribution differences? Electroenceph. clin. Neurophysiol., 46: 409415. Julesz, B. (1971) Foundations of Cyclopean Perception. University of Chicago Press, Chicago, 111. Lehmann, D. and Julesz, B. (1978) Lateralized cortical potentials evoked in humans by dynamic random-dot stereograms. Vision Res., 18: 1265-1271. Lehmann, D. and Skrandies, W. (1979) Multichannel evoked potential fields show different properties of human upper and lower hemiretina systems. Exp. Bruin Res., 35: 151-159. Lehmann, D., Meles, H. P. and Mir, Z. (1977) Average multichannel EEG potential fields evoked from upper and lower hemi-retina: latency differences. Electroenceph. din. Neurophysiol., 43: 725-731. Lehmann, D., Skrandies, W. and Lindenmaier, C. (1978) Sustained cortical potentials evoked in humans by
260 binocularly correlated, uncorrelated and disparate dynamic random-dot stimuli. Neurosci. Lett., 10: 129-1 34. Lettvin, J. Y., Maturana, H. R., McCulloch, W. S. and Pitts, W. H. (1959) What the frog’s eye tells the frog’s brain. Proc. Inst. Radio Engng,47: 1940-1951. MacKay, D. M. (1957) Moving visual images produced by regular stationary patterns. Nature (Lond.), 180: 849-850. MacKay, D. M. (1970) Perception and brain function. In The Neurosciences: Second Study Program, F. 0. Schmitt (Ed.), Rockefeller University Press, New York, pp. 303-316. MacKay, D. M. (1973) Lateral interaction between neural channels sensitive to texture density? Nature (Lond.), 245: 159-161. MacKay, D. M. (1974) Texture-density contrast: inhibition or adaptation? Nature (Lond.), 249: 8 6 8 7 . MacKay, D. M. (1977) Adaptation of evoked potentials by patterns of texture-contrast. Exp. Bruin Res., 29: 149-1 53. MacKay, D. M. (1978a) The time-course of induction of complementary images. Vision Res., 18: 913-916. MacKay, D. M. (1978b) The Dynamics of Perception. In Cerebral Correlates of Conscious Experience, P. A. Buser and A. Rouged-Buser (Eds.), Elsevier, Amsterdam, pp. 53-68. MacKay, D. M. (1979) Clues to the site of origin of the complementary image. Nature (Lond.), 279: 553. MacKay, D. M., Gerrits, H. J. M. and Stassen, H. P. W. (1979) Interaction of stabilized retinal patterns with spatial visual noise. Vision Rex, 19: 713-716. Smith, A. T. and Jeffreys, D. A. (1978) Evoked potential evidence of adaptation to spatial Fourier components in human vision. Nature (Lond.), 274: 156158. Spekreijse, H. (1966) Analysis of EEG Responses to Dfluse and to Patterned Light in the Human (Thesis, University of Amsterdam). Junk, The Hague.
In thinking over the topic of this lecture, the image that came to mind was that of tunnelling the Alps. I remember as a child being impressed by the precision with which those making a tunnel from the north side were able by dead reckoning to proceed so accurately to meet those tunnelling from the south side that only a fraction of a metre separated the two when they met. Over the past 3 decades, we have all been making strenuous efforts to form a working link between those studying sensory processing in the brain at the single unit level and those studying sensory evoked potentials. The enterprise is still at a relatively early stage, and pessimism is sometimes expressed as to whether our respective tunnels will ever meet. Are we even starting at the right levels, let alone heading in convergent directions? This is the question that challenges us in the present session. I regard it as very much an unresolved question; but I would like to offer one or two suggestions as to ways in which the process of fruitful convergence might be accelerated. Because Dr. McCallum will be dealing mainly with the auditory system in his review, I shall take most of my examples from our recent work at Keele on the visual system, with just a brief reference to auditory information processing at the end. THE IMPORTANCE O F GLOBAL PROPERTIES OF THE VISUAL FIELD Visual neurophysiology took a great leap forward when Hubel and Wiesel (1959, 1962), working on cat, and Lettvin et al. (1959), working on the frog, discovered that many single units in the visual system are specifically sensitive to bars or edges of luminance-contrast with specific orientations on the retina. In area 17 of cat, for example, Hubel and Wiesel identified cells which they called respectively “simple”, “complex” and “hypercomplex”. Simple cells had relatively restricted receptive fields, in which elongated excitatory and inhibitory zones flanked one another and gave rise to a strong preference for appropriately oriented bars or edges of luminance moving across them. Complex cells, which also had well-defined orientation preferences, were responsive over a wider area and were presumed to depend on simple cells for their inputs. In consequence of these discoveries and others like them, most of us in recent years have worked with a model of the primary visual cortex as a serial information processing mechanism. Simple and complex cells were thought of as serially related “feature detectors”, each of which signified by its firing one particular geometrical detail of the
246 figure of excitation on the retina. Models of pattern recognition were canvassed in which these simple and complex cells served as the first stages in a hierarchic pattern classifier leading to unique specificity of single cells for the most complex patterns. On the other hand, it has long been pointed out by such writers as Gibson (1950) that for many biologically significant purposes the visual system ought to be sensitive to more global properties of the visual field, such as texture density and gradients of texture density. He noted, for example, that an object camouflaged against a background of similar texture can be instantly perceived, and its form recognized, if it moves against its background so as to generate discontinuities of texture-motion on the retina. This, together with the discovery of a number of psychaphysical phenomena in which texture density played a role somewhat analogous to luminance (MacKay, 1973, 1974), led me to wonder how the simple and complex cells of area 17 might respond to visual patterns presented as contrasts of texture motion. If they were the classifying elements in pattern recognition, would they show the same orientation specificity for patterns of texture contrast as for patterns of luminance contrast? My colleague Dr. Peter Hammond and I are still at an early stage in this investigation, and what follows are only samples from an on-going story (Hammond and MacKay, 1975, 1977, 1978; Groos et al., 1976; Hammond, 1978). RESPONSES OF CAT STRIATE CELLS TO TEXTURED STIMULI Fig. 1 shows some examples of the stimuli we use. These are produced electronically on a CRT display capable of a basic picture frame rate up to 400/sec. Our first tests employed bars of “static visual noise”, variable in orientation and dimensions, which could be swept to and fro over a statistically similar static noise background. Cats were lightly anaesthetized with N,O/O, mixtures supplemented with pentobarbitone. What we found may be summarized as follows: (1) Simple cells were unresponsive to all forms of visual noise presented alone, except when individual blobs of texture were large enough to function as luminance stimuli in their own right. About 70%, however, showed a change in responsiveness to conventional black-and-white bar stimuli, when these were presented against backgrounds of static noise which moved with the bar rather than reniaining stationary. Luminancebar responses were depressed by background texture motion in the majority of cells (84%), but were actually enhanced in some instances (16%). (2) In contrast, all complex cells were found to respond to some extent to bars of static visual noise moving over stationary backgrounds of similar texture. Significantly, however, most of them showed no sensitivity to the orientation of the stimulus boundary. They responded as well or better to motion of a whole field of static noise. This showed that (a) whatever part simple and complex cells in area 17 may play in the recognition of patterns outlined by luminance contrast, they do not play the same role in relation to recognition of patterns outlined by texture-motion contrast; (b) since complex cells can be excited (to a greater or lesser extent) by stimuli to which simple cells are unresponsive, most complex cells must receive a visual input independent of that from simple cells. (3) In a number of cases, the ocular dominance of complex cells was different for bar stimuli and for visual texture. (4) Complex cells fell into two overlapping groups with regard to their relative
247
-.-
Fig. I . Examples of textured stimuli used.
sensitivity to light or dark bars and visual noise. Extreme examples were insensitive to conventional light or dark bar or edge stimuli, while responding briskly to moving noise. The optimal velocity for noise was generally lower than for bar stimuli. ( 5 ) In many complex cells, the preferred directions for motion of noise and of an optimally oriented black/white bar were strikinglydissimilar. Fig. 2 shows some examples of the responsiveness as a function of direction of stimulus motion for each class of stimuli. In a number of cases the polar diagram for visual noise showed two maxima, which might be separated by 90"or more. Fig. 3 gives some indication of the range of directional disparities observed. (6) The greatest sensitivity to textured stimuli was found among deep-layer complex cells. We have also found these cells to be most tolerant of misalignment of the components of a stimulating luminance line. Superficial-layer complex cells were found to be less tolerant of misalignment of stimulus elements and were less responsive to moving noise.
248
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Fig. 3. Histogram showing the range of disparitiesin preferred direction of motion found by Hammond (1978).
249
It follows from ill this that the firing of many such complex cells cannot in isolation signify to the central nervous system either the geometrical orientation or the direction of motion of the exciting stimulus. This does not necessarily mean that they have no function in relation to pattern recognition, but it does mean that they can function in this respect only in co-operation with their neighbours, possibly on a basis of crosscorrelation of firing patterns with respect to space and time. MODULATION O F RESPONSIVENESS OF SIMPLE CELLS BY BACKGROUND MOTION In order to investigate the effect of moving backgrounds on the responsiveness of simple cells, Hammond and MacKay (1978) have been using electronically generated stimuli of the sort schematized in Fig. 4. A black or white bar is oscillated continuously (ljsec) at the preferred orientation and location of the cell to be investigated. A moveable patch (shown occupied by a chequerboard in Fig. 4) is alternately held stationary and moved with the stimulus. The general texture background can also be moved with the stimulus or held stationary. Fig. 5 shows the result of one experiment in which the patch was centred on the receptive field, and its size varied in the direction of the length of the stimulating bar (i.e., along the receptive field axis). It will be seen that the suppressive effect of the moving background continues to increase for a patch much longer than the receptive field as conventionally defined. Fig. 6 shows the result of keeping the patch constant in size and exploring its effectiveness by displacing it along the length axis of the field. To our surprise, it turned out in a number of cases (though not in all) that a moving patch well outside the conventional receptive field could actually enhance the responsiveness of a simple cell, although suppressing it when closer to the centre of the receptive field.
Fig. 4. Sample of stimuli used to investigate modulation of responsiveness of simple cells by areas of moving textured background.
250
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Fig. 6 . Modulating power of a narrow patch as a function of distance from receptive field centre. Receptive field length approx. 1".
IMPLICATIONS FOR EP WORK Preliminary though some of them are, these results have a number of implications relevant in the present context. First, in line with other evidence (Cleland et al., 1976), they indicate that we must reckon with parallel as well as serial processing of information in the primary visual cortex. While they do nothing to refute the accepted belief that most complex cells are hierarchically dependent on simple cells, they do suggest that most complex cells also receive an input independent of simple cells. Secondly, they show that different classes of visual stimulus may excite the binocularly driven cell population to differing extents. In other words, some cells may be dominated by bar stimuli to one eye but equally sensitive to texture stimuli in either eye. Thirdly, cells excited by bars moving in one direction may also be excitable by patterns of random texture moving in quite different directions. Fourthly, even simple cells may receive extensive lateral influences from neighbouring cells, so that their response characteristics can be modulated from a considerable distance. In general, then, we must beware of thinking of even the primary visual cortex as an assembly of independent descriptors of the geometrical features lying within sharply
25 1
localized windows. We must be prepared for elements of the visual network to interact co-operatively and to show specific responses to global properties of the stimulus field. All this throws a new light on the possible usefulness of evoked brain potentials as reflectors of such co-operative interactions. I would like now to turn to a few examples, also drawn from recent work in our laboratory, of EP data which point in the same direction. Most of these concern early (5G200 msec) components of the visual evoked response, though I will touch on one auditory example at the end. SENSITIVITY OF PATTERN-EVOKED RESPONSES TO GLOBAL FEATURES Although the electrogenesis of the visual EP is still a matter of debate, it might have seemed plausible to expect the patternevoked components of the response to reflect something like the total length of contour in the visual field. Fig. 7, however, from the early work of Jeffreys (1969) shows that this expectation is not fulfilled. Instead, Jeffreys found that the removal of contour from a uniform pattern such as (b) in Fig. 7, so as to give a more broken outline (d), enhanced the evoked response. Isolated squares (f) produced a larger respbnse than a chequerboard (g), and so on. For the generator of this EP component, then, what seems to matter is not the number of “contour-detectors’’ excited, but what we might call the “brokenness” - a more global property - of the stimulating pattern. Despite appearances, even the early response complex shown in Fig. 7 is not unitary. It was discovered by Spekreijse (1966) that the “steady-state” potentials evoked by rhythmic presentation of a given pattern could be considerably reduced by pre-exposing an outline of the same pattern drawn with thin lines. An analogous finding has been 250 m s
PTm
Fig. 7. Averaged,scalp responses to lower half-field presentation of patterns shown, of varying degrees of “brokenness”. Active electrodes 5 cm above inion, 6 an apart. (Courtesy of D. A. Jeffreys.)
252 exploited by Jeffreys (1977) in relation to transient VEPs to show that the positive and negative peaks in Fig. 8 are due to the superposition of independently variable components, named by him C1 and C2, which turn out to have different scalp distributions (Jeffreys, 1971). When a pattern of isolated squares is presented tachistoscopically after pre-exposure to the outlined squares shown in Fig. 8c, there is relatively little diminution of the first peak, but the second is heavily attenuated. Conversely, when the outlined figure alone is tachistoscopically presented, the evoked response (b) contains a large C2 component but practically no C1. The same figure shows that although a grating of horizontal bars (d) evokes only a small second peak, the first peak is of comparable magnitude. Pre-exposure to an outline of the grating, as shown at Fig. 8e, again attenuates C2 selectively. By varying the relative location of the pre-adapting outlines, Jeffreys has shown that exact superposition is not necessary. Moreover, as shown in Fig. 9, a uniform grid of lines does not have the same adapting effect on the EP to isolated squares, even though its contours cover all the contours of the stimulating figure. This shows that the preadapting effect is not due simply to pre-excitation of contour detectors. As shown in Fig. 9, even the component C2 for a chequerboard stimulus is not suppressed by a grid of lines (although C3 is much attenuated). Once again, the implication seems to be that more global properties of the figure of excitation, rather than its local specification in terms of the presence or absence of contours, play a part in determining the response. What then of the earlier component Cl? The fact that it shows relatively less adaptation after pre-exposure of stimulus outlines suggests that part of it originates in a sub-population responsive to modulation of luminance at lower spatial frequencies. Jeffreys and Smith (1979) have confirmed this hypothesis by measuring the adapting effect (on C1) of pre-exposure to patterns with luminance components having similar orientations at low spatial frequencies but disparate orientations at high frequencies. Fig. 10 for example shows that C1 for a square-wave grating was reduced more by preexposure to a chequerboard at 45" (when the fundamental frequencies had the same Clll
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253 CIII
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Fig. 10. Typical transient VEP waveform elicited: (a) by a horizontal 2.0 c/degree grating in the left half of the field without adaptation; (b) after adaptation to a horizontally orientated chequerboard; (c) after adaptation to an obliquely orientated chequerboard.
orientation) than when the edges of the chequerboard were parallel to those of the grating. Fig. 11 shows that this also applied when the adapting and evoking stimuli were interchanged*. Jeffreys has also found the method of selective adaptation useful in identifying corresponding components of the EP complex from stimuli presented to different retinal areas. Pattern EPs from upper and lower half-fields, for example, show a characteristic reversal of polarity (at the same latency) in many subjects. Jeffreys and Axford (1972) showed by a simple model that for C1 this reversal could be attributed to the infolding of the striate cortex around the calcarine fissure. Lehmann et al. (1977) and Lehmann and *Since this paper was presented, DeValois et al. ( J . Physiol. (Lond.), 1979, 291: 483-505) have published an investigation of simple and complex cells of striate cortex using grating and chequerboard stimuli, with somewhat analogous results. This is encouragingly consonant with the hypothesis of Jeffreys and Axford (1972) that C1 originates in striate cortex, though it may be questioned whether these findings oblige us to follow DeValois et al. in considering single cells as “spatial-frequency filters” rather than “edge-detectors”.
254
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Spatial frequency (cycles per degree) Fig. 11. Mean C1 amplitude (n=6) elicited by: (a) a horizontal grating of the size shown after adaptation to a or an obliquely oriented chequerboard ( 0 )of appropriate size; (b) horizontally orientated chequerboard ( 0) a chequerboard of the size shown after adaptation to a horizontal grating ( 0) or an oblique grating ( 0 )of appropriate size.
Skrandies (1979) have recently suggested that it could instead be due to large differences in latency between upper- and lower-field responses; but Jeffreys and Smith (1979), using the pre-adaptation method, have been able to confirm that the components with similar adaptive characteristics do have the same latencies but opposite polarities in the two cases (Fig. 12).
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Fig. 12. Comparison of sensitivity of upper and lower half-field VEPs to preexposure of outlined patterns. Pre-exposure selectively affects peaks of opposite polarity but the same latency, suggesting that only the orientations of the sources stimulated by upper and lower half-fields are different (see Jeffreys and Smith, 1979).
255 One way of interpreting the attenuation of C2 (and C3) by pre-exposure to pattern outlines would be to postulate that these components were generated by mechanisms specific for recognition of the pattern as such, which would presumably be activated by the pre-exposure, and so become adapted. This hypothesis, however, is not supported by an experiment which was suggested by the work with Hammond described above. Instead of using thin black lines to outline the pre-exposed adapting pattern, I used patterns outlined by the contrast between static and dynamic random visual noise, generated on the electronic display used for our neurophysiological experiments (MacKay, 1977). This made it possible to pre-expose the subject to a pattern of isolated squares without any contrast in mean luminance (Fig. 13). The response to the subsequent stimulus pattern of black isolated squares was indeed reduced (Fig. 14a4), but when the pre-adapting noise-contrast pattern itself was used as an evoking stimulus the resulting waveform (e) had not at all the same time course as the EP to black squares*. Furthermore, the EP to a grating of dynamic noise stripes on an isoluminant static noise background (f) was as large as that to the isolated noise squares of Fig. 13 again in contrast to the EP to a black-and-white grating, which was much smaller than that to black squares (compare Fig. 14h and i). This does not mean, however, that only the area of texture matters, and not its pattern; for the EPs to a whole-field transition
Fig. 13. Typical pattern of static noise (SN) squares, each subtending0.6", on a background of dynamic noise (DN), here shown as uniform grey. Fixation at point X, top centre. *The EP to thinly outlined squares is more similar in form but different in latency (Jeffreys, personal communication).
256
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Fig. 14. a, c: VEPs to 25 msec presentations of pattern of isolated 0.6" black squares as in Fig. 12a, after preexposure to (left to right): (i) dynamic noise (DN); (ii) matching pattern of squares of static noise (SN) on DN, (iii) ditto but DN squares on SN; (iv) SN. Subjects (a) DAJ and (c) IEB. Amplifier time constant 0.3 sec. b, d: as above, but squares subtending0.3" with 0.3" gaps. e: VEPs to 60 msec presentations of (left) isolated 0.6" SN squares on steady DN background; (right) ditto, but DN squares on steady SN. Subject DAJ. f: as e, but with unbroken horizontal strips instead of squares. g: as e, but whole field changing from (left) DN to SN; (right) SN to DN. h: VEPs to 25 msec presentations of isolated 0.6"black squares with uniform white pre-exposure field. Subject DAJ (compare e). i: as h, but with unbroken black horizontal strips instead of squares (compare f). Two successive averages superimposed in each case.
257 from static to dynamic noise or vice versa are significantly smaller (Fig. 14g). Complex though this picture is, it at least shows that the mechanism responsible for the adapting response to pre-exposure is not indifferent to the mode of presentation of the preadapting pattern, and hence is unlikely to be the one responsible for the recognition of geometrical form regardless of mode of presentation.
EP INDICATIONS O F SENSORY CROSS-CORRELATION The experiments of Lehmann and Julesz (1978) have shown that significant EPs can be elicited by changes in the perceptual depth (retinal image disparity) of stereoscopically presented surfaces of dynamic noise. This raises the question whether more basic forms of sensory cross-correlation, which there are theoretical reasons for expecting to be ubiquitous in the CNS (MacKay, 1978b), are accompanied by detectable changes in scalp potential. On the visual front, Lehmann et al. (1978) have reported that when a field of dynamic noise in one eye is switched from incoherence to coherence with a similar field in the other eye, a sustained (anterior negative) shift in scalp potential occurs. Similar results have been found independently by Julesz (Julesz, B., personal communication) and myself, and Fig. 15 shows a typical result from one of my subjects. In preliminary experiments on the auditory system I have also found analogous evoked responses to a transition from incoherence to coherence, or vice versa, between random noise inputs to the two ears (Fig. 16). Again the change to coherence evokes an anterior-negative potential shift. It can be seen from the lower traces in Fig. 16 that the responses to a 100 msec pulse of silence in noise (a), or noise in silence (b), are quite different in form and latency. COH
INC
Fig. 15. Distribution of averaged scalp potentials (N = 80) evoked by transitions between coherence (COH) and incoherence (INC) of dynamic visual noise fields (30 frames/sec) in left and right eyes.
258 COH
I NC
b
Fig. 16. Upper traces: average (N = 100) of anterior/posterior midline potentials evoked by a 100 msec pulse of coherence (COH) or incoherence (INC) between noise signals to left and right ears (two successive averages superimposed). Lower traces: controls using a 100 msec pulse of (a) silence out of a background of incoherent binaural noise; (b) incoherent binaural noise against a silent background.
DISCUSSION Much of the neurophysiological work I have described is still at an early stage; but I would like to offer the following points for discussion: (a) There is now clear evidence of parallel as well as serial processing of visual information even as early as the primary projection area 17. (b) Different classes of stimuli may have different effective “weights” for binocular units in striate cortex, e.g., some cells may even be functionally monocular for black-and-white bar stimuli yet binocular for textured stimuli. (c) The same cell may have different preferred directions of motion for different geometrical classes of stimuli (e.g. bars versus static noise). (d) Both simple and complex cells of area 17 may receive lateral inputs and be dynamically dependent on relatively remote neighbours for their detailed response characteristics from moment to moment. This evidence should perhaps warn us against thinking even of the primary visual cortex as an assembly of independent “descriptors looking through windows” (featuredetecting cells whose firing would signifv to the CNS the presence of their favourite feature). Doubtless most of these cells have preferred features, and their firing surely contributes information as to the external stimuli exciting them; but it seems likely that the decoding of that information requires much computational interaction with signals from other cells of the same level. We would also be unwise, I think, to restrict our idea of such interaction to simple notions of “sharpening”, “peak-picking’’ and the like - as if the cells were “really” trying to do a feature-detecting operation but were bungling their selectivejob. There are many hints from perceptual phenomena (MacKay, 1957, 1970, 1978a,b; Julesz, 1971) that we must be prepared to find the neural network responding co-operatively to global properties of the stimulus field. There are reasons for suspecting that this co-operativity may in particular cases amount to transient rearrangement of local couplings (MacKay, 1957, 1970, 1978a, 1979; MacKay et al., 1979).
259 All this, it seems to me, gives a new importance to the EP recording method, and a fresh incentive towards its refinement, not just as a second best to the recording of single action potentials, but as in principle one of the few ways in which we can hope to detect and follow neurophysiologically the progress of spatially extensive co-operative processes in the neural network. Without some such means, it seems more than possible that a realistic understanding of sensory information processing will be denied us. ACKNOWLEDGEMENTS
I thank my colleagues Peter Hammond and Aled Jeffreys both for helpful comments on this manuscript and for the major contributions they have made to its illustrative material. The support of the Medical and Science Research Councils is gratefully acknowledged. REFERENCES Cleland, B. G., Levick, W. R., Morstyn, R. and Wagner, H.G. (1976) Lateral geniculate relay of slowly conducting retinal afferents to cat visual cortex. J. Physiol. (Lond.), 255: 299-320. Gibson, J. J. (1950) The Perception of the Visual World. Houghton MifAin, Boston, Mass. Groos, G. A., Hammond, P. and MacKay, D. M. (1976) Polar responsiveness of complex cells in cat striate cortex to motion of bars and of textured patterns. J. Physiol, (Lond.), 260: 47-48P. Hammond, P. (1978) Directional tuning of complex cells in area 17 of the feline visual cortex. J. Physio/. (Lond.), 285: 479491. Hammond, P. and MacKay, D. M. (1975) Differential responses of cat visual cortical cells to textured stimuli. Exp. Bruin Res., 22: 427430. Hammond, P. and MacKay, D. M. (1977) Differential responsiveness of simple and complex cells in cat striate cortex to visual texture. Exp. Bruin R e x , 3 0 275-296. Hammond, P. and MacKay, D. M. (1978) Modulation of simple cell activity in cat by moving textured backgrounds. J. Physiol. (Lond.), 284: 1 17P. Hubel, D. H. and Wiesel, T. N. (1959) Receptive fields of single neurones in the cat’s striate cortex. J. Physio/. (Lond.), 148: 576591. Hubel, D. H. and Wiesel, T. N. (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. (Lond.), 160: 106154. Jeffreys, D. A, (1969) Discussion. In Evoked Bruin Potentiuls us Indicators of Sensory Information Processing, D. M. MacKay (Ed.), Neurosci. Res. Bull., 7 216. Jeffreys, D. A. (197 1) Cortical source locations of pattern-related visual evoked potentials recorded from the human scalp. Nature (Lond.), 229: 502-504. Jeffreys, D. A. (1977) The physiological significance of pattern visual evoked potentials. In Visual Evoked Potentials in Man: New Developments. J. E. Desmedt (Ed.), Clarendon Press, Oxford, pp. 134-167. Jeffreys, D. A. and Axford, J. G. (1972) Source locations of pattern-specific components of human visual evoked potentials. I. Component of striate cortical origin. Exp. Bruin Res., 16: 1-21. Jeffreys, D. A. and Smith, A. T. (1979) The polarity inversion of scalp potentials evoked by upper and lower half-field stimulus patterns: latency or surface distribution differences? Electroenceph. clin. Neurophysiol., 46: 409415. Julesz, B. (1971) Foundations of Cyclopean Perception. University of Chicago Press, Chicago, 111. Lehmann, D. and Julesz, B. (1978) Lateralized cortical potentials evoked in humans by dynamic random-dot stereograms. Vision Res., 18: 1265-1271. Lehmann, D. and Skrandies, W. (1979) Multichannel evoked potential fields show different properties of human upper and lower hemiretina systems. Exp. Bruin Res., 35: 151-159. Lehmann, D., Meles, H. P. and Mir, Z. (1977) Average multichannel EEG potential fields evoked from upper and lower hemi-retina: latency differences. Electroenceph. din. Neurophysiol., 43: 725-731. Lehmann, D., Skrandies, W. and Lindenmaier, C. (1978) Sustained cortical potentials evoked in humans by
260 binocularly correlated, uncorrelated and disparate dynamic random-dot stimuli. Neurosci. Lett., 10: 129-1 34. Lettvin, J. Y., Maturana, H. R., McCulloch, W. S. and Pitts, W. H. (1959) What the frog’s eye tells the frog’s brain. Proc. Inst. Radio Engng,47: 1940-1951. MacKay, D. M. (1957) Moving visual images produced by regular stationary patterns. Nature (Lond.), 180: 849-850. MacKay, D. M. (1970) Perception and brain function. In The Neurosciences: Second Study Program, F. 0. Schmitt (Ed.), Rockefeller University Press, New York, pp. 303-316. MacKay, D. M. (1973) Lateral interaction between neural channels sensitive to texture density? Nature (Lond.), 245: 159-161. MacKay, D. M. (1974) Texture-density contrast: inhibition or adaptation? Nature (Lond.), 249: 8 6 8 7 . MacKay, D. M. (1977) Adaptation of evoked potentials by patterns of texture-contrast. Exp. Bruin Res., 29: 149-1 53. MacKay, D. M. (1978a) The time-course of induction of complementary images. Vision Res., 18: 913-916. MacKay, D. M. (1978b) The Dynamics of Perception. In Cerebral Correlates of Conscious Experience, P. A. Buser and A. Rouged-Buser (Eds.), Elsevier, Amsterdam, pp. 53-68. MacKay, D. M. (1979) Clues to the site of origin of the complementary image. Nature (Lond.), 279: 553. MacKay, D. M., Gerrits, H. J. M. and Stassen, H. P. W. (1979) Interaction of stabilized retinal patterns with spatial visual noise. Vision Rex, 19: 713-716. Smith, A. T. and Jeffreys, D. A. (1978) Evoked potential evidence of adaptation to spatial Fourier components in human vision. Nature (Lond.), 274: 156158. Spekreijse, H. (1966) Analysis of EEG Responses to Dfluse and to Patterned Light in the Human (Thesis, University of Amsterdam). Junk, The Hague.